Add Hollow Mode to easy multiAngle (#936)
- Global control to enable or disable angle prompts - Added `Hollow Mode` for more intuitive visualization - Removed label quantity limit; now supports unlimited additions - Label addition button will copy parameters from the selected page - Double-clicking any face of the cube quickly switches camera angles for easier operation - 全局控制是否添加角度提示词 - 新增了`镂空模式`，可更直观地展示 - 去除标签限制个数，可添加无数个 - 标签添加按钮将复制选中页的参数 - 双击正方体的每一面可以快速切换摄像机角度，便于操作
This commit is contained in:
yolain
@@ -0,0 +1,605 @@
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---
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applyTo: "**/*.py"
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description: "ComfyUI v3 Node Examples"
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---
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# ComfyUI v3 Node Examples
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Real-world examples of v3 nodes demonstrating various features and patterns.
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## Basic Examples
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### Simple Image Processor
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```python
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from comfy_api.latest import io, ui
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import torch
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class ImageInvertV3(io.ComfyNode):
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"""Simple node that inverts image colors."""
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@classmethod
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def define_schema(cls):
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return io.Schema(
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node_id="ImageInvert_v3",
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display_name="Invert Image",
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category="image/filters",
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description="Inverts the colors of an image",
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inputs=[
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io.Image.Input("image", tooltip="Image to invert")
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],
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outputs=[
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io.Image.Output("inverted", tooltip="Inverted image")
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]
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)
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@classmethod
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def execute(cls, image):
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# Invert: 1.0 - image
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inverted = 1.0 - image
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return io.NodeOutput(inverted, ui=ui.PreviewImage(inverted))
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```
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### Math Operations
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```python
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class MathOperationV3(io.ComfyNode):
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"""Performs math operations on two values."""
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@classmethod
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def define_schema(cls):
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return io.Schema(
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node_id="MathOperation_v3",
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display_name="Math Operation",
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category="utils/math",
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inputs=[
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io.Float.Input("a", default=0.0),
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io.Float.Input("b", default=0.0),
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io.Combo.Input("operation",
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options=["add", "subtract", "multiply", "divide", "power"],
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default="add"
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)
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],
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outputs=[
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io.Float.Output("result")
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]
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)
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@classmethod
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def execute(cls, a, b, operation):
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operations = {
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"add": a + b,
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"subtract": a - b,
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"multiply": a * b,
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"divide": a / b if b != 0 else 0,
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"power": a ** b
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}
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result = operations[operation]
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return io.NodeOutput(result)
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```
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## Async Examples
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### API Integration
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```python
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import aiohttp
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class TextGeneratorV3(io.ComfyNode):
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"""Generates text using external API."""
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@classmethod
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def define_schema(cls):
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return io.Schema(
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node_id="TextGenerator_v3",
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display_name="AI Text Generator",
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category="text/generation",
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inputs=[
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io.String.Input("prompt", multiline=True),
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io.String.Input("api_url", default="http://localhost:11434/api/generate"),
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io.String.Input("model", default="llama2"),
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io.Float.Input("temperature", default=0.7, min=0.0, max=2.0)
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],
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outputs=[
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io.String.Output("generated_text")
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]
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)
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@classmethod
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async def execute(cls, prompt, api_url, model, temperature):
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async with aiohttp.ClientSession() as session:
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payload = {
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"model": model,
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"prompt": prompt,
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"temperature": temperature,
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"stream": False
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}
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async with session.post(api_url, json=payload) as response:
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if response.status == 200:
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data = await response.json()
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text = data.get("response", "")
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return io.NodeOutput(text)
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else:
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raise RuntimeError(f"API error: {response.status}")
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```
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### Batch Processing with Progress
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```python
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from comfy.utils import ProgressBar
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import asyncio
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class BatchImageProcessorV3(io.ComfyNode):
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"""Processes images in batch with progress tracking."""
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@classmethod
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def define_schema(cls):
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return io.Schema(
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node_id="BatchImageProcessor_v3",
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display_name="Batch Image Processor",
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category="image/batch",
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inputs=[
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io.Image.Input("images"),
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io.Float.Input("process_time", default=0.1, min=0.01, max=1.0,
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tooltip="Simulated processing time per image")
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],
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outputs=[
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io.Image.Output("processed")
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],
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hidden=[io.Hidden.unique_id]
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)
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@classmethod
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async def execute(cls, images, process_time, **kwargs):
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batch_size = images.shape[0]
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pbar = ProgressBar(batch_size, node_id=cls.hidden.unique_id)
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processed = []
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for i in range(batch_size):
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# Simulate async processing
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await asyncio.sleep(process_time)
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# Example: Apply blur
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import torch.nn.functional as F
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blurred = F.gaussian_blur(images[i:i+1], kernel_size=5)
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processed.append(blurred)
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pbar.update(1)
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result = torch.cat(processed, dim=0)
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return io.NodeOutput(result, ui=ui.PreviewImage(result))
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```
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## Advanced Examples
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### Model Loader with Resources
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```python
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import folder_paths
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import comfy.utils
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import comfy.sd
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class CheckpointLoaderV3(io.ComfyNode):
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"""Loads checkpoint models with caching."""
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@classmethod
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def define_schema(cls):
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return io.Schema(
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node_id="CheckpointLoader_v3",
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display_name="Load Checkpoint",
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category="loaders",
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inputs=[
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io.Combo.Input("ckpt_name",
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options=folder_paths.get_filename_list("checkpoints"),
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tooltip="Select checkpoint to load"
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)
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],
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outputs=[
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io.Model.Output("model"),
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io.Clip.Output("clip"),
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io.Vae.Output("vae")
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]
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)
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@classmethod
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def execute(cls, ckpt_name):
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# Use resource caching
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ckpt = cls.resources.get(
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resources.TorchDictFolderFilename("checkpoints", ckpt_name)
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)
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# Load components
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model, clip, vae = comfy.sd.load_checkpoint_guess_config(
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ckpt,
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embedding_directory=folder_paths.get_folder_paths("embeddings")
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)
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return io.NodeOutput(model, clip, vae)
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```
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### State Management Example
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```python
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class IterativeRefinerV3(io.ComfyNode):
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"""Refines images iteratively with state tracking."""
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@classmethod
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def define_schema(cls):
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return io.Schema(
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node_id="IterativeRefiner_v3",
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display_name="Iterative Refiner",
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category="image/processing",
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inputs=[
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io.Image.Input("image"),
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io.Int.Input("iterations", default=3, min=1, max=10),
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io.Boolean.Input("reset", default=False,
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tooltip="Reset refinement history")
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],
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outputs=[
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io.Image.Output("refined"),
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io.Int.Output("total_iterations")
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]
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)
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@classmethod
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def execute(cls, image, iterations, reset):
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# Initialize or reset state
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if reset or cls.state.history is None:
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cls.state.history = []
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cls.state.total_iterations = 0
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# Get last refined image or use input
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current = cls.state.history[-1] if cls.state.history else image
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# Iterative refinement
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for i in range(iterations):
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# Example: Progressive sharpening
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import torch.nn.functional as F
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kernel = torch.tensor([[-1,-1,-1],
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[-1, 9,-1],
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[-1,-1,-1]], dtype=torch.float32)
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kernel = kernel.view(1, 1, 3, 3)
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kernel = kernel.repeat(current.shape[-1], 1, 1, 1)
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current = current.permute(0, 3, 1, 2)
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sharpened = F.conv2d(current, kernel, padding=1, groups=current.shape[1])
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current = sharpened.permute(0, 2, 3, 1)
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current = torch.clamp(current, 0, 1)
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# Update state
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cls.state.history.append(current)
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cls.state.total_iterations += iterations
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# Keep history size manageable
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if len(cls.state.history) > 10:
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cls.state.history.pop(0)
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return io.NodeOutput(
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current,
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cls.state.total_iterations,
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ui=ui.PreviewImage(current)
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)
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```
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### Dynamic Inputs Example
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```python
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class ImageBlenderV3(io.ComfyNode):
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"""Blends multiple images with weights."""
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@classmethod
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def define_schema(cls):
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return io.Schema(
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node_id="ImageBlender_v3",
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display_name="Image Blender",
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category="image/blend",
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inputs=[
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io.AutoGrowDynamicInput("images",
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template_input=io.Image.Input("image"),
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min=2,
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max=8
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),
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io.Combo.Input("mode",
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options=["average", "weighted", "max", "min"],
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default="average"
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)
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],
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outputs=[
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io.Image.Output("blended")
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]
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)
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@classmethod
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def execute(cls, mode, **kwargs):
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# Collect all image inputs
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images = []
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for key, value in sorted(kwargs.items()):
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if key.startswith("image"):
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images.append(value)
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if not images:
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raise ValueError("No images provided")
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# Stack images
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stacked = torch.stack(images, dim=0)
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# Blend based on mode
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if mode == "average":
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blended = torch.mean(stacked, dim=0)
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elif mode == "weighted":
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# Simple linear weighting
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weights = torch.linspace(1, 0.1, len(images))
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weights = weights / weights.sum()
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weights = weights.view(-1, 1, 1, 1, 1)
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blended = (stacked * weights).sum(dim=0)
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elif mode == "max":
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blended = torch.max(stacked, dim=0)[0]
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elif mode == "min":
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blended = torch.min(stacked, dim=0)[0]
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return io.NodeOutput(blended, ui=ui.PreviewImage(blended))
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```
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### Multi-Type Input Example
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```python
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class UniversalInverterV3(io.ComfyNode):
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"""Inverts images, masks, or conditioning."""
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@classmethod
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def define_schema(cls):
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return io.Schema(
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node_id="UniversalInverter_v3",
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display_name="Universal Inverter",
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category="utils/invert",
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inputs=[
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io.MultiType.Input("input",
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types=[io.Image, io.Mask, io.Conditioning]
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),
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io.Float.Input("strength", default=1.0, min=0.0, max=1.0)
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],
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outputs=[
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io.MultiType.Output("inverted",
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types=[io.Image, io.Mask, io.Conditioning]
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)
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]
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)
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@classmethod
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def execute(cls, input, strength):
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# Detect input type and process accordingly
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if isinstance(input, torch.Tensor):
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# Image or Mask
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if input.dim() == 4: # Image [B,H,W,C]
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inverted = 1.0 - input
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inverted = input + (inverted - input) * strength
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return io.NodeOutput(inverted, ui=ui.PreviewImage(inverted))
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else: # Mask [H,W] or [B,H,W]
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inverted = 1.0 - input
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inverted = input + (inverted - input) * strength
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return io.NodeOutput(inverted, ui=ui.PreviewMask(inverted))
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elif isinstance(input, list): # Conditioning
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# Invert conditioning strength
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inverted = []
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for cond, data in input:
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new_data = data.copy()
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if 'strength' in new_data:
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new_data['strength'] = 1.0 - new_data['strength']
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inverted.append((cond, new_data))
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return io.NodeOutput(inverted)
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else:
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raise ValueError(f"Unsupported input type: {type(input)}")
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```
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### Custom Type Example
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```python
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class CustomDataProcessorV3(io.ComfyNode):
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"""Processes custom data types."""
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@classmethod
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def define_schema(cls):
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return io.Schema(
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node_id="CustomDataProcessor_v3",
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display_name="Custom Data Processor",
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category="utils/custom",
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inputs=[
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io.Custom(io_type="MY_CUSTOM_TYPE").Input("custom_data",,
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tooltip="Custom data type input"
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),
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io.Float.Input("scale", default=1.0, min=0.1, max=10.0)
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],
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outputs=[
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io.Custom(io_type="MY_CUSTOM_TYPE").Output("processed_data",
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tooltip="Processed custom data"
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)
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]
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)
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@classmethod
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def execute(cls, custom_data, scale):
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# Process custom data type
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# Assuming custom_data is a dict with 'value' and 'metadata'
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processed = {
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'value': custom_data.get('value', 0) * scale,
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'metadata': custom_data.get('metadata', {}),
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'processed': True
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}
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return io.NodeOutput(processed)
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```
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|
||||
## Process Isolation Example
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||||
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||||
### Node with Specific Dependencies
|
||||
|
||||
```python
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# manifest.yaml
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"""
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||||
name: scientific_processor
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||||
version: 1.0.0
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||||
dependencies:
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- numpy==1.24.0 # Specific older version needed
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||||
- scipy==1.10.0
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||||
- scikit-image==0.20.0
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isolated: true
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share_torch: true
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"""
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||||
|
||||
# __init__.py
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from comfy_api.latest import io, io.ComfyNode, io.Schema
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import numpy as np
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from skimage import filters
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||||
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||||
class ScientificProcessorV3(io.ComfyNode):
|
||||
"""Image processing with scientific libraries."""
|
||||
|
||||
@classmethod
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||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="ScientificProcessor_v3",
|
||||
display_name="Scientific Processor",
|
||||
category="image/scientific",
|
||||
inputs=[
|
||||
io.Image.Input("image"),
|
||||
io.Combo.Input("filter_type",
|
||||
options=["gaussian", "sobel", "laplacian", "butterworth"],
|
||||
default="gaussian"
|
||||
),
|
||||
io.Float.Input("sigma", default=1.0, min=0.1, max=10.0)
|
||||
],
|
||||
outputs=[
|
||||
io.Image.Output("filtered")
|
||||
]
|
||||
)
|
||||
|
||||
@classmethod
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||||
def execute(cls, image, filter_type, sigma):
|
||||
# Convert to numpy
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||||
img_np = image.cpu().numpy()
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||||
batch_size = img_np.shape[0]
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||||
|
||||
results = []
|
||||
for i in range(batch_size):
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img = img_np[i]
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||||
|
||||
if filter_type == "gaussian":
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||||
filtered = filters.gaussian(img, sigma=sigma, channel_axis=-1)
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||||
elif filter_type == "sobel":
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||||
gray = np.mean(img, axis=-1)
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||||
filtered = filters.sobel(gray)
|
||||
filtered = np.stack([filtered]*3, axis=-1)
|
||||
elif filter_type == "laplacian":
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||||
gray = np.mean(img, axis=-1)
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||||
filtered = filters.laplace(gray)
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||||
filtered = np.stack([filtered]*3, axis=-1)
|
||||
elif filter_type == "butterworth":
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||||
# Frequency domain filtering
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||||
for c in range(3):
|
||||
channel = img[:,:,c]
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||||
fft = np.fft.fft2(channel)
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||||
fft_shift = np.fft.fftshift(fft)
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||||
# Apply Butterworth filter
|
||||
H = 1 / (1 + (D/sigma)**4) # Simplified
|
||||
filtered_fft = fft_shift * H
|
||||
filtered[:,:,c] = np.real(np.fft.ifft2(np.fft.ifftshift(filtered_fft)))
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||||
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||||
results.append(filtered)
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||||
|
||||
# Convert back to tensor
|
||||
result = torch.from_numpy(np.stack(results)).float()
|
||||
return io.NodeOutput(result, ui=ui.PreviewImage(result))
|
||||
|
||||
# Entry point for pyisolate
|
||||
from pyisolate import ExtensionBase
|
||||
|
||||
class ScientificExtension(ExtensionBase):
|
||||
def on_module_loaded(self, module):
|
||||
self.nodes = {
|
||||
"ScientificProcessor_v3": ScientificProcessorV3
|
||||
}
|
||||
|
||||
def create_extension():
|
||||
return ScientificExtension()
|
||||
```
|
||||
|
||||
## Complete Workflow Example
|
||||
|
||||
```python
|
||||
class TextToImageWorkflowV3(io.ComfyNode):
|
||||
"""Complete text-to-image workflow in one node."""
|
||||
|
||||
@classmethod
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="TextToImageWorkflow_v3",
|
||||
display_name="Text to Image Workflow",
|
||||
category="workflows",
|
||||
description="All-in-one text to image generation",
|
||||
inputs=[
|
||||
io.String.Input("positive_prompt", multiline=True),
|
||||
io.String.Input("negative_prompt", multiline=True, default=""),
|
||||
io.Model.Input("model"),
|
||||
io.Clip.Input("clip"),
|
||||
io.Vae.Input("vae"),
|
||||
io.Int.Input("seed", default=0, min=0, max=0xffffffffffffffff),
|
||||
io.Int.Input("steps", default=20, min=1, max=150),
|
||||
io.Float.Input("cfg", default=7.0, min=0.0, max=30.0),
|
||||
io.Combo.Input("sampler_name",
|
||||
options=comfy.samplers.KSampler.SAMPLERS,
|
||||
default="euler"
|
||||
),
|
||||
io.Combo.Input("scheduler",
|
||||
options=comfy.samplers.KSampler.SCHEDULERS,
|
||||
default="normal"
|
||||
),
|
||||
io.Int.Input("width", default=1024, min=64, max=8192, step=8),
|
||||
io.Int.Input("height", default=1024, min=64, max=8192, step=8),
|
||||
io.Int.Input("batch_size", default=1, min=1, max=64)
|
||||
],
|
||||
outputs=[
|
||||
io.Image.Output("images", is_output_list=True),
|
||||
io.Latent.Output("latents")
|
||||
],
|
||||
is_output_node=True
|
||||
)
|
||||
|
||||
@classmethod
|
||||
async def execute(cls, positive_prompt, negative_prompt, model, clip, vae,
|
||||
seed, steps, cfg, sampler_name, scheduler,
|
||||
width, height, batch_size):
|
||||
import comfy.samplers
|
||||
|
||||
# Encode prompts
|
||||
positive_cond = clip.encode_from_text(positive_prompt)
|
||||
negative_cond = clip.encode_from_text(negative_prompt)
|
||||
|
||||
# Create empty latent
|
||||
latent = torch.zeros([batch_size, 4, height // 8, width // 8])
|
||||
|
||||
# Set up sampler
|
||||
sampler = comfy.samplers.KSampler(
|
||||
model, steps, cfg, sampler_name, scheduler,
|
||||
positive_cond, negative_cond, latent,
|
||||
denoise=1.0, seed=seed
|
||||
)
|
||||
|
||||
# Sample with progress callback
|
||||
def callback(step, x0, x, total_steps):
|
||||
# Could update progress here
|
||||
pass
|
||||
|
||||
samples = sampler.sample(latent, callback=callback)
|
||||
|
||||
# Decode latents
|
||||
images = vae.decode(samples["samples"])
|
||||
|
||||
return io.NodeOutput(
|
||||
images,
|
||||
samples,
|
||||
ui=ui.PreviewImage(images)
|
||||
)
|
||||
```
|
||||
@@ -0,0 +1,530 @@
|
||||
---
|
||||
applyTo: "**/*.py"
|
||||
description: "ComfyUI v3 Migration Guide"
|
||||
---
|
||||
|
||||
# ComfyUI v3 Migration Guide
|
||||
|
||||
This guide helps developers migrate existing v1 nodes to the new v3 schema and take advantage of async execution and process isolation.
|
||||
|
||||
## Quick Start: The Core Changes
|
||||
|
||||
1. **Inherit from `io.ComfyNode`**: Your node class now subclasses `io.ComfyNode`.
|
||||
2. **Use `define_schema`**: All metadata (`INPUT_TYPES`, `CATEGORY`, etc.) moves into a single `@classmethod def define_schema(cls)` that returns an `io.Schema` object.
|
||||
3. **Use `execute`**: The main logic function is now always a `@classmethod def execute(cls, ...)` method.
|
||||
4. **Use Typed I/O**: Inputs and outputs are now strongly-typed objects from the `io` module (e.g., `io.Image.Input(...)`).
|
||||
5. **Return `NodeOutput`**: The `execute` method must return an `io.NodeOutput` instance.
|
||||
6. **Use `NODES_LIST`**: Node registration is done by adding the class to a `NODES_LIST` at the end of the file, replacing `NODE_CLASS_MAPPINGS` and `NODE_DISPLAY_NAME_MAPPINGS`.
|
||||
|
||||
## Step-by-Step Migration
|
||||
|
||||
### Step 1: Class Definition and Schema
|
||||
|
||||
**V1:**
|
||||
```python
|
||||
class Canny:
|
||||
CATEGORY = "image/preprocessors"
|
||||
FUNCTION = "detect_edge"
|
||||
RETURN_TYPES = ("IMAGE",)
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {"required": {
|
||||
"image": ("IMAGE",),
|
||||
"low_threshold": ("FLOAT", {"default": 0.4}),
|
||||
"high_threshold": ("FLOAT", {"default": 0.8}),
|
||||
}}
|
||||
|
||||
def detect_edge(self, image, low_threshold, high_threshold):
|
||||
# ... logic ...
|
||||
return (img_out,)
|
||||
|
||||
NODE_CLASS_MAPPINGS = {"Canny": Canny}
|
||||
```
|
||||
|
||||
**V3:**
|
||||
```python
|
||||
from comfy_api.latest import io
|
||||
|
||||
class Canny(io.ComfyNode):
|
||||
@classmethod
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="Canny_V3",
|
||||
category="image/preprocessors",
|
||||
inputs=[
|
||||
io.Image.Input("image"),
|
||||
io.Float.Input("low_threshold", default=0.4),
|
||||
io.Float.Input("high_threshold", default=0.8),
|
||||
],
|
||||
outputs=[io.Image.Output()],
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def execute(cls, image, low_threshold, high_threshold):
|
||||
# ... logic ...
|
||||
return io.NodeOutput(img_out)
|
||||
|
||||
NODES_LIST = [Canny]
|
||||
```
|
||||
|
||||
### Step 2: Naming and Registration (`node_id`, `display_name`, `NODES_LIST`)
|
||||
|
||||
This is a critical step for ensuring your V3 node coexists with or replaces the V1 version correctly.
|
||||
|
||||
1. **Remove Old Mappings**: Delete the `NODE_CLASS_MAPPINGS` and `NODE_DISPLAY_NAME_MAPPINGS` dictionaries.
|
||||
2. **Create `NODES_LIST`**: Create a new list called `NODES_LIST` and add your V3 class to it.
|
||||
3. **Set `node_id`**: The `node_id` in `Schema` **must** be the key from the old `NODE_CLASS_MAPPINGS`.
|
||||
4. **Set `display_name` (Conditionally)**:
|
||||
- Check if a key existed in the old `NODE_DISPLAY_NAME_MAPPINGS`.
|
||||
- **If yes**: Set `display_name` to that value.
|
||||
- **If no**: **Omit** the `display_name` parameter from `Schema` entirely.
|
||||
|
||||
**Example:**
|
||||
|
||||
**V1 Registration:**
|
||||
```python
|
||||
NODE_CLASS_MAPPINGS = {
|
||||
"APG": APG,
|
||||
}
|
||||
NODE_DISPLAY_NAME_MAPPINGS = {
|
||||
"APG": "Adaptive Projected Guidance",
|
||||
}
|
||||
```
|
||||
|
||||
**V3 `define_schema`:**
|
||||
```python
|
||||
@classmethod
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="APG_V3", # From MAPPINGS key + "_V3"
|
||||
display_name="Adaptive Projected Guidance _V3", # From DISPLAY MAPPINGS value + " _V3"
|
||||
# ... other parameters
|
||||
)
|
||||
|
||||
NODES_LIST = [APG] # ... at end of file
|
||||
```
|
||||
|
||||
### Step 3: Converting I/O
|
||||
|
||||
| V1 Type (`string`) | V3 Class (`io.<Type>`) | Common `Input()` Options (as keyword arguments) |
|
||||
|:-----------------------|:------------------------|:--------------------------------------------------------------------------|
|
||||
| `STRING` | `io.String` | `default`, `multiline`, `dynamic_prompts`, `placeholder` |
|
||||
| `INT` | `io.Int` | `default`, `min`, `max`, `step`, `display_mode`, `control_after_generate` |
|
||||
| `FLOAT` | `io.Float` | `default`, `min`, `max`, `step`, `round`, `display_mode` |
|
||||
| `BOOLEAN` | `io.Boolean` | `default`, `label_on`, `label_off` |
|
||||
| `COMBO` | `io.Combo` | `options`, `default`, `upload`, `image_folder`, `remote` |
|
||||
| (custom) | `io.MultiCombo` | `options`, `default`, `placeholder`, `chip` |
|
||||
| `IMAGE` | `io.Image` | |
|
||||
| `MASK` | `io.Mask` | |
|
||||
| `MESH` | `io.Mesh` | |
|
||||
| `HOOKS` | `io.Hooks` | |
|
||||
| `HOOK_KEYFRAMES` | `io.HookKeyframes` | |
|
||||
| `LATENT` | `io.Latent` | |
|
||||
| `LATENT_OPERATION` | `io.LatentOperation` | |
|
||||
| `LOAD3D_CAMERA` | `io.Load3DCamera` | |
|
||||
| `LOAD_3D` | `io.Load3D` | |
|
||||
| `LOAD_3D_ANIMATION` | `io.Load3DAnimation` | |
|
||||
| `LOSS_MAP` | `io.LossMap` | |
|
||||
| `LORA_MODEL` | `io.LoraModel` | |
|
||||
| `CONDITIONING` | `io.Conditioning` | |
|
||||
| `CLIP` | `io.Clip` | |
|
||||
| `CLIP_VISION_OUTPUT` | `io.ClipVisionOutput` | |
|
||||
| `NOISE` | `io.Noise` | |
|
||||
| `VAE` | `io.Vae` | |
|
||||
| `MODEL` | `io.Model` | |
|
||||
| `CONTROL_NET` | `io.ControlNet` | |
|
||||
| `SAMPLER` | `io.Sampler` | |
|
||||
| `SIGMAS` | `io.Sigmas` | |
|
||||
| `GUIDER` | `io.Guider` | |
|
||||
| `CLIP_VISION` | `io.ClipVision` | |
|
||||
| `UPSCALE_MODEL` | `io.UpscaleModel` | |
|
||||
| `AUDIO` | `io.Audio` | |
|
||||
| `VIDEO` | `io.Video` | |
|
||||
| `VOXEL` | `io.Voxel` | |
|
||||
| `WAN_CAMERA_EMBEDDING` | `io.WanCameraEmbedding` | |
|
||||
| `WEBCAM` | `io.Webcam` | `default`, `socketless` |
|
||||
| `*` | `io.AnyType` | Used for inputs that can accept any type, like the PreviewAny node. |
|
||||
|
||||
#### Advanced Input Types
|
||||
|
||||
**MultiType Input (accepts multiple types):**
|
||||
```python
|
||||
io.MultiType.Input("input", types=[io.Mask, io.Float, io.Int], optional=True)
|
||||
```
|
||||
|
||||
**Combo with Remote Options:**
|
||||
```python
|
||||
io.Combo.Input(
|
||||
"lora_name",
|
||||
options=folder_paths.get_filename_list("loras"),
|
||||
tooltip="The name of the LoRA."
|
||||
)
|
||||
```
|
||||
|
||||
**Optional Parameters:**
|
||||
```python
|
||||
io.Boolean.Input(
|
||||
"case_sensitive",
|
||||
default=True,
|
||||
optional=True, # Makes this input optional
|
||||
tooltip="Whether to use case-sensitive matching"
|
||||
)
|
||||
```
|
||||
|
||||
|
||||
### Step 4: Migrating Logic
|
||||
|
||||
- **Execution Method**: Rename your old `FUNCTION` to `execute` and make it a `@classmethod`.
|
||||
- **Return Value**: Wrap your return tuple in `io.NodeOutput()`. For UI updates, use the `ui` keyword argument: `io.NodeOutput(ui=ui.PreviewImage(image))`.
|
||||
- **State**: Replace `self.variable` with `cls.state.variable`.
|
||||
- **Hidden Inputs**: Replace `prompt` and `unique_id` parameters with `cls.hidden.prompt` and `cls.hidden.unique_id`. Request them in the schema with `hidden=[io.Hidden.prompt, io.Hidden.unique_id]`.
|
||||
- **Optional Methods**: `IS_CHANGED` becomes `fingerprint_inputs`, and `VALIDATE_INPUTS` becomes `validate_inputs`. Both should be `@classmethod`.
|
||||
|
||||
## Common Migration Patterns
|
||||
|
||||
### 1. Hidden Inputs
|
||||
|
||||
**V1:**
|
||||
```python
|
||||
"hidden": {
|
||||
"prompt": "PROMPT",
|
||||
"unique_id": "UNIQUE_ID"
|
||||
}
|
||||
|
||||
def execute(self, ..., prompt=None, unique_id=None):
|
||||
... # Use hidden inputs
|
||||
```
|
||||
|
||||
**V3:**
|
||||
```python
|
||||
hidden=[
|
||||
io.Hidden.prompt,
|
||||
io.Hidden.unique_id
|
||||
]
|
||||
|
||||
@classmethod
|
||||
def execute(cls, ...):
|
||||
# Access via **cls**
|
||||
prompt = cls.hidden.prompt
|
||||
unique_id = cls.hidden.unique_id
|
||||
```
|
||||
|
||||
### 2. State Management
|
||||
|
||||
**V1:**
|
||||
```python
|
||||
def __init__(self):
|
||||
self.last_seed = None
|
||||
self.cache = {}
|
||||
|
||||
def execute(self, seed, ...):
|
||||
if seed != self.last_seed:
|
||||
self.cache.clear()
|
||||
self.last_seed = seed
|
||||
```
|
||||
|
||||
**V3:**
|
||||
```python
|
||||
@classmethod
|
||||
def execute(cls, seed, ...):
|
||||
if cls.state.last_seed != seed:
|
||||
cls.state.cache = {}
|
||||
cls.state.last_seed = seed
|
||||
```
|
||||
|
||||
### 3. UI Output
|
||||
|
||||
**V1:**
|
||||
```python
|
||||
def execute(self, image):
|
||||
# Save preview manually
|
||||
preview = save_temp_image(image)
|
||||
return {"ui": {"images": preview}, "result": (image,)}
|
||||
```
|
||||
|
||||
**V3:**
|
||||
```python
|
||||
@classmethod
|
||||
def execute(cls, image):
|
||||
return io.NodeOutput(image, ui=ui.PreviewImage(image))
|
||||
```
|
||||
|
||||
### 4. Dynamic Inputs
|
||||
|
||||
**V1:**
|
||||
```python
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
# Complex logic to generate dynamic inputs
|
||||
inputs = {"required": {}}
|
||||
for i in range(get_dynamic_count()):
|
||||
inputs["required"][f"input_{i}"] = ("IMAGE",)
|
||||
return inputs
|
||||
```
|
||||
|
||||
**V3:**
|
||||
```python
|
||||
inputs=[
|
||||
io.AutoGrowDynamic.Input("images",
|
||||
template_input=io.Image.Input("image"),
|
||||
min=1,
|
||||
max=10
|
||||
)
|
||||
]
|
||||
```
|
||||
|
||||
### 5. Resource Loading
|
||||
|
||||
**V1:**
|
||||
```python
|
||||
def execute(self, model_name):
|
||||
# Direct file loading
|
||||
model_path = folder_paths.get_full_path("checkpoints", model_name)
|
||||
model = comfy.utils.load_torch_file(model_path)
|
||||
```
|
||||
|
||||
**V3:**
|
||||
```python
|
||||
from comfy_api.latest import resources
|
||||
|
||||
@classmethod
|
||||
def execute(cls, model_name):
|
||||
# Cached resource loading
|
||||
model = cls.resources.get(
|
||||
resources.TorchDictFolderFilename("checkpoints", model_name)
|
||||
)
|
||||
```
|
||||
|
||||
## Making Nodes Async
|
||||
|
||||
### Basic Async Node
|
||||
|
||||
```python
|
||||
class AsyncNodeV3(io.ComfyNode):
|
||||
@classmethod
|
||||
async def execute(cls, image, url):
|
||||
# Network request without blocking
|
||||
async with aiohttp.ClientSession() as session:
|
||||
async with session.get(url) as response:
|
||||
data = await response.json()
|
||||
|
||||
# Process with the data
|
||||
result = process_image_with_data(image, data)
|
||||
return io.NodeOutput(result)
|
||||
```
|
||||
|
||||
### Progress Tracking
|
||||
|
||||
```python
|
||||
@classmethod
|
||||
async def execute(cls, images, unique_id):
|
||||
from comfy.utils import ProgressBar
|
||||
|
||||
batch_size = images.shape[0]
|
||||
pbar = ProgressBar(batch_size, node_id=unique_id)
|
||||
|
||||
results = []
|
||||
for i in range(batch_size):
|
||||
# Async processing
|
||||
result = await process_single(images[i])
|
||||
results.append(result)
|
||||
pbar.update(1)
|
||||
|
||||
return io.NodeOutput(torch.cat(results))
|
||||
```
|
||||
|
||||
## Enabling Process Isolation
|
||||
|
||||
### 1. Create manifest.yaml
|
||||
|
||||
```yaml
|
||||
name: my_custom_nodes
|
||||
version: 1.0.0
|
||||
description: My custom node collection
|
||||
author: Your Name
|
||||
dependencies:
|
||||
- numpy==1.26.4
|
||||
- scikit-image>=0.22.0
|
||||
- opencv-python
|
||||
isolated: true
|
||||
share_torch: true
|
||||
```
|
||||
|
||||
### 2. Update __init__.py
|
||||
|
||||
```python
|
||||
from pyisolate import ExtensionBase
|
||||
from .nodes import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
|
||||
|
||||
class MyNodesExtension(ExtensionBase):
|
||||
def on_module_loaded(self, module):
|
||||
# Nodes are automatically registered
|
||||
pass
|
||||
|
||||
async def get_node_mappings(self):
|
||||
return NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
|
||||
|
||||
# Extension entry point
|
||||
def create_extension():
|
||||
return MyNodesExtension()
|
||||
```
|
||||
|
||||
## Practical Migration Examples
|
||||
|
||||
### Complete String Node Conversion
|
||||
|
||||
This example shows a full conversion of the StringConcatenate node from v1 to v3:
|
||||
|
||||
**V1 Implementation:**
|
||||
```python
|
||||
class StringConcatenate():
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"string_a": (IO.STRING, {"multiline": True}),
|
||||
"string_b": (IO.STRING, {"multiline": True}),
|
||||
"delimiter": (IO.STRING, {"multiline": False, "default": ""})
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = (IO.STRING,)
|
||||
FUNCTION = "execute"
|
||||
CATEGORY = "utils/string"
|
||||
|
||||
def execute(self, string_a, string_b, delimiter, **kwargs):
|
||||
return delimiter.join((string_a, string_b)),
|
||||
```
|
||||
|
||||
**V3 Implementation:**
|
||||
```python
|
||||
from comfy_api.latest import io, ui
|
||||
|
||||
class StringConcatenate(io.ComfyNode):
|
||||
"""Concatenates two strings with an optional delimiter between them."""
|
||||
|
||||
@classmethod
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="StringConcatenate",
|
||||
display_name="String Concatenate",
|
||||
category="utils/string",
|
||||
description="Concatenates two strings together with an optional delimiter between them.",
|
||||
inputs=[
|
||||
io.String.Input(
|
||||
"string_a",
|
||||
display_name="String A",
|
||||
multiline=True,
|
||||
tooltip="The first string to concatenate"
|
||||
),
|
||||
io.String.Input(
|
||||
"string_b",
|
||||
display_name="String B",
|
||||
multiline=True,
|
||||
tooltip="The second string to concatenate"
|
||||
),
|
||||
io.String.Input(
|
||||
"delimiter",
|
||||
display_name="Delimiter",
|
||||
default="",
|
||||
multiline=False,
|
||||
tooltip="The delimiter to insert between the two strings (empty by default)"
|
||||
),
|
||||
],
|
||||
outputs=[
|
||||
io.String.Output(
|
||||
"concatenated",
|
||||
display_name="Concatenated String",
|
||||
tooltip="The result of concatenating string_a and string_b with the delimiter"
|
||||
),
|
||||
],
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def execute(cls, string_a: str, string_b: str, delimiter: str) -> io.NodeOutput:
|
||||
"""Concatenates two strings with an optional delimiter."""
|
||||
result = delimiter.join((string_a, string_b))
|
||||
return io.NodeOutput(result)
|
||||
```
|
||||
|
||||
### Replacing V1 Nodes Strategy
|
||||
|
||||
When replacing v1 nodes with v3 implementations:
|
||||
|
||||
1. **Keep Original Node Names**: Don't add "V3" suffix to maintain compatibility
|
||||
2. **Preserve All Parameters**: Keep same parameter names and defaults
|
||||
3. **Maintain Return Structure**: v3 automatically generates v1-compatible returns
|
||||
4. **Test Workflow Compatibility**: Ensure existing workflows continue to work
|
||||
|
||||
Example migration workflow:
|
||||
```bash
|
||||
# 1. Create new branch
|
||||
git checkout -b v3-node-migration
|
||||
|
||||
# 2. Backup original
|
||||
cp nodes_original.py nodes_original.py.bak
|
||||
|
||||
# 3. Replace with v3 version
|
||||
cp nodes_v3.py nodes_original.py
|
||||
|
||||
# 4. Test with existing workflows
|
||||
comfy-cli test-workflows ./test-workflows/
|
||||
```
|
||||
|
||||
## Testing Your Migration
|
||||
|
||||
### 1. Backward Compatibility Test
|
||||
|
||||
```python
|
||||
# Your v3 node should work with v1 calls
|
||||
def test_v1_compatibility():
|
||||
node = MyNodeV3()
|
||||
inputs = node.INPUT_TYPES()
|
||||
assert "required" in inputs
|
||||
assert hasattr(node, "FUNCTION")
|
||||
assert hasattr(node, "RETURN_TYPES")
|
||||
```
|
||||
|
||||
### 2. Async Execution Test
|
||||
|
||||
```python
|
||||
import asyncio
|
||||
|
||||
async def test_async_execution():
|
||||
result = await MyAsyncNode.execute(image=test_image)
|
||||
assert result is not None
|
||||
```
|
||||
|
||||
### 3. Isolation Test
|
||||
|
||||
```bash
|
||||
# Test with conflicting dependencies
|
||||
comfy-cli test-node --isolated my_custom_nodes
|
||||
```
|
||||
|
||||
## Best Practices
|
||||
|
||||
1. **Keep nodes stateless** - Use `cls.state` for any mutable data
|
||||
2. **Make I/O operations async** - Network, disk, database operations
|
||||
3. **Use resource caching** - Via `cls.resources.get()`
|
||||
4. **Declare all dependencies** - In manifest.yaml
|
||||
5. **Test both sync and async** - Ensure compatibility
|
||||
6. **Document type changes** - Help users update workflows
|
||||
|
||||
## Common Issues
|
||||
|
||||
### Issue: State not persisting
|
||||
**Solution:** Use `cls.state` instead of instance variables
|
||||
|
||||
### Issue: Hidden inputs not working
|
||||
**Solution:** Access via `cls.hidden.unique_id` not function parameters
|
||||
|
||||
### Issue: Async not executing
|
||||
**Solution:** Ensure method is `async def` and use `await` for async calls
|
||||
|
||||
### Issue: Import errors in isolation
|
||||
**Solution:** Add all dependencies to manifest.yaml
|
||||
|
||||
### Issue: Tensors not sharing
|
||||
**Solution:** Enable `share_torch: true` in manifest.yaml
|
||||
@@ -0,0 +1,635 @@
|
||||
---
|
||||
applyTo: "**/*.py"
|
||||
description: "ComfyUI v3 API Reference"
|
||||
---
|
||||
|
||||
# ComfyUI v3 API Reference
|
||||
|
||||
Complete reference for the ComfyUI v3 node API, including all types, methods, and decorators.
|
||||
|
||||
## Core Classes
|
||||
|
||||
### ComfyNodeV3
|
||||
|
||||
Base class for all v3 nodes.
|
||||
|
||||
```python
|
||||
from comfy_api.latest import io
|
||||
|
||||
class CustomNode(io.ComfyNode):
|
||||
# Class properties set during execution
|
||||
state: NodeState # Persistent state storage
|
||||
resources: Resources # Resource loader with caching
|
||||
hidden: HiddenHolder # Access to hidden inputs
|
||||
|
||||
@classmethod
|
||||
@abstractmethod
|
||||
def define_schema(cls) -> io.ComfyNode:
|
||||
"""Define node schema. Must be overridden."""
|
||||
pass
|
||||
|
||||
@classmethod
|
||||
@abstractmethod
|
||||
def execute(cls, **kwargs) -> io.NodeOutput:
|
||||
"""Execute node logic. Can be async."""
|
||||
pass
|
||||
|
||||
@classmethod
|
||||
def validate_inputs(cls, **kwargs) -> bool:
|
||||
"""Optional: Validate inputs before execution."""
|
||||
pass
|
||||
|
||||
@classmethod
|
||||
def fingerprint_inputs(cls, **kwargs) -> Any:
|
||||
"""Optional: Generate a fingerprint for caching."""
|
||||
pass
|
||||
|
||||
@classmethod
|
||||
def GET_SERIALIZERS(cls) -> list[Serializer]:
|
||||
"""Optional: Define custom serializers."""
|
||||
return []
|
||||
```
|
||||
|
||||
### io.ComfyNode
|
||||
|
||||
Node definition schema.
|
||||
|
||||
```python
|
||||
@dataclass
|
||||
class io.ComfyNode:
|
||||
node_id: str # Globally unique ID
|
||||
display_name: str = None # UI display name
|
||||
category: str = "sd" # Node category
|
||||
inputs: list[InputV3] = None # Input definitions
|
||||
outputs: list[OutputV3] = None # Output definitions
|
||||
hidden: list[Hidden] = None # Hidden inputs
|
||||
description: str = "" # Tooltip description
|
||||
is_input_list: bool = False # Handle list inputs
|
||||
is_output_node: bool = False # Force execution
|
||||
is_deprecated: bool = False # Mark as deprecated
|
||||
is_experimental: bool = False # Mark as experimental
|
||||
is_api_node: bool = False # API node flag
|
||||
not_idempotent: bool = False # Disable caching
|
||||
```
|
||||
|
||||
### NodeOutput
|
||||
|
||||
Structured return value from `execute`.
|
||||
|
||||
```python
|
||||
class NodeOutput:
|
||||
def __init__(
|
||||
self,
|
||||
*args: Any, # Output values
|
||||
ui: UIOutput | dict = None, # UI elements
|
||||
expand: dict = None, # Subgraph expansion
|
||||
block_execution: str = None # Execution blocker
|
||||
):
|
||||
pass
|
||||
```
|
||||
|
||||
## Input Types
|
||||
|
||||
### Basic Inputs
|
||||
|
||||
```python
|
||||
# Integer input
|
||||
io.Int.Input(
|
||||
id: str,
|
||||
display_name: str = None,
|
||||
optional: bool = False,
|
||||
tooltip: str = None,
|
||||
lazy: bool = None,
|
||||
default: int = None,
|
||||
min: int = None,
|
||||
max: int = None,
|
||||
step: int = None,
|
||||
control_after_generate: bool = None,
|
||||
display_mode: NumberDisplay = None,
|
||||
socketless: bool = None,
|
||||
force_input: bool = None
|
||||
)
|
||||
|
||||
# Float input
|
||||
io.Float.Input(
|
||||
id: str,
|
||||
display_name: str = None,
|
||||
optional: bool = False,
|
||||
tooltip: str = None,
|
||||
lazy: bool = None,
|
||||
default: float = None,
|
||||
min: float = None,
|
||||
max: float = None,
|
||||
step: float = None,
|
||||
round: float = None,
|
||||
display_mode: NumberDisplay = None,
|
||||
socketless: bool = None,
|
||||
force_input: bool = None
|
||||
)
|
||||
|
||||
# String input
|
||||
io.String.Input(
|
||||
id: str,
|
||||
display_name: str = None,
|
||||
optional: bool = False,
|
||||
tooltip: str = None,
|
||||
lazy: bool = None,
|
||||
multiline: bool = False,
|
||||
placeholder: str = None,
|
||||
default: str = None,
|
||||
dynamic_prompts: bool = None,
|
||||
socketless: bool = None,
|
||||
force_input: bool = None
|
||||
)
|
||||
|
||||
# Boolean input
|
||||
io.Boolean.Input(
|
||||
id: str,
|
||||
display_name: str = None,
|
||||
optional: bool = False,
|
||||
tooltip: str = None,
|
||||
lazy: bool = None,
|
||||
default: bool = None,
|
||||
label_on: str = None,
|
||||
label_off: str = None,
|
||||
socketless: bool = None,
|
||||
force_input: bool = None
|
||||
)
|
||||
|
||||
# Combo (dropdown) input
|
||||
io.Combo.Input(
|
||||
id: str,
|
||||
options: list[str] = None,
|
||||
display_name: str = None,
|
||||
optional: bool = False,
|
||||
tooltip: str = None,
|
||||
lazy: bool = None,
|
||||
default: str = None,
|
||||
control_after_generate: bool = None,
|
||||
upload: UploadType = None,
|
||||
image_folder: FolderType = None,
|
||||
remote: RemoteOptions = None,
|
||||
socketless: bool = None
|
||||
)
|
||||
|
||||
# Multi-select combo
|
||||
io.MultiCombo.Input(
|
||||
id: str,
|
||||
options: list[str],
|
||||
display_name: str = None,
|
||||
optional: bool = False,
|
||||
tooltip: str = None,
|
||||
lazy: bool = None,
|
||||
default: list[str] = None,
|
||||
placeholder: str = None,
|
||||
chip: bool = None,
|
||||
control_after_generate: bool = None,
|
||||
socketless: bool = None
|
||||
)
|
||||
# cusotm type
|
||||
io.Custom(io_type="MY_TYPE").Input(
|
||||
id: str,
|
||||
display_name: str = None,
|
||||
optional: bool = False,
|
||||
tooltip: str = None,
|
||||
lazy: bool = None,
|
||||
placeholder: str = None,
|
||||
)
|
||||
```
|
||||
|
||||
### ComfyUI Types
|
||||
|
||||
```python
|
||||
# Core types
|
||||
io.Image.Input(id, ...) # Type: torch.Tensor [B,H,W,C]
|
||||
io.Mask.Input(id, ...) # Type: torch.Tensor [H,W] or [B,H,W]
|
||||
io.Latent.Input(id, ...) # Type: dict with 'samples' tensor
|
||||
io.Conditioning.Input(id, ...) # Type: list[tuple[tensor, dict]]
|
||||
io.Model.Input(id, ...) # Type: ModelPatcher
|
||||
io.Clip.Input(id, ...) # Type: CLIP
|
||||
io.Vae.Input(id, ...) # Type: VAE
|
||||
io.ControlNet.Input(id, ...) # Type: ControlNet
|
||||
|
||||
# Sampling types
|
||||
io.Sampler.Input(id, ...) # Type: Sampler
|
||||
io.Sigmas.Input(id, ...) # Type: torch.Tensor
|
||||
io.Noise.Input(id, ...) # Type: torch.Tensor
|
||||
io.Guider.Input(id, ...) # Type: CFGGuider
|
||||
|
||||
# Additional types
|
||||
io.ClipVision.Input(id, ...) # Type: ClipVisionModel
|
||||
io.ClipVisionOutput.Input(id, ...) # Type: ClipVisionOutput
|
||||
io.StyleModel.Input(id, ...) # Type: StyleModel
|
||||
io.Gligen.Input(id, ...) # Type: ModelPatcher
|
||||
io.UpscaleModel.Input(id, ...) # Type: ImageModelDescriptor
|
||||
io.Audio.Input(id, ...) # Type: dict with 'waveform' and 'sample_rate'
|
||||
io.Video.Input(id, ...) # Type: VideoInput
|
||||
io.Webcam.Input(id, ...) # Type: str (filepath)
|
||||
io.WanCameraEmbedding.Input(id, ...) # Type: torch.Tensor
|
||||
io.LoraModel.Input(id, ...) # Type: dict[str, Tensor]
|
||||
io.Hooks.Input(id, ...) # Type: HookGroup
|
||||
io.HookKeyframes.Input(id, ...) # Type: HookKeyframeGroup
|
||||
io.SVG.Input(id, ...) # Type: SVG (custom class)
|
||||
io.Voxel.Input(id, ...) # Type: Voxel data (custom class)
|
||||
io.Mesh.Input(id, ...) # Type: Mesh data (custom class)
|
||||
```
|
||||
|
||||
### Advanced Inputs
|
||||
|
||||
```python
|
||||
# Multi-type input (accepts multiple types)
|
||||
io.MultiType.Input(
|
||||
id: str | InputV3, # Can override from existing input
|
||||
types: list[type[ComfyType]],
|
||||
display_name: str = None,
|
||||
optional: bool = False,
|
||||
tooltip: str = None,
|
||||
lazy: bool = None
|
||||
)
|
||||
|
||||
# Dynamic growing input
|
||||
io.AutogrowDynamic.Input(
|
||||
id: str,
|
||||
template_input: InputV3, # Template for each new input
|
||||
min: int = 1, # Minimum inputs
|
||||
max: int = None # Maximum inputs
|
||||
)
|
||||
|
||||
# Custom type
|
||||
@io.comfytype(io_type="MY_CUSTOM")
|
||||
class MyCustom:
|
||||
Type = MyDataClass
|
||||
class Input(io.InputV3):
|
||||
...
|
||||
class Output(io.OutputV3):
|
||||
...
|
||||
```
|
||||
|
||||
## Output Types
|
||||
|
||||
```python
|
||||
# Basic output
|
||||
io.Image.Output(
|
||||
id: str = None,
|
||||
display_name: str = None,
|
||||
tooltip: str = None,
|
||||
is_output_list: bool = False # Output is list
|
||||
)
|
||||
|
||||
# All ComfyUI types have corresponding outputs
|
||||
io.Mask.Output(id, ...)
|
||||
io.Latent.Output(id, ...)
|
||||
io.Model.Output(id, ...)
|
||||
io.Clip.Output(id, ...)
|
||||
io.Vae.Output(id, ...)
|
||||
io.Conditioning.Output(id, ...)
|
||||
io.String.Output(id, ...)
|
||||
io.Int.Output(id, ...)
|
||||
io.Float.Output(id, ...)
|
||||
io.Boolean.Output(id, ...)
|
||||
# ... etc
|
||||
```
|
||||
|
||||
## Hidden Inputs
|
||||
|
||||
```python
|
||||
from comfy_api.latest import Hidden
|
||||
|
||||
# Available hidden inputs
|
||||
Hidden.unique_id # Node's unique ID
|
||||
Hidden.prompt # Complete prompt
|
||||
Hidden.extra_pnginfo # PNG metadata dict
|
||||
Hidden.dynprompt # Dynamic prompt object
|
||||
Hidden.auth_token_comfy_org # ComfyOrg auth token
|
||||
Hidden.api_key_comfy_org # ComfyOrg API key
|
||||
|
||||
# Usage in schema
|
||||
hidden=[
|
||||
Hidden.unique_id,
|
||||
Hidden.prompt
|
||||
]
|
||||
|
||||
# Access in execute
|
||||
unique_id = cls.hidden.unique_id
|
||||
prompt = cls.hidden.prompt
|
||||
```
|
||||
|
||||
## State Management
|
||||
|
||||
```python
|
||||
# NodeState interface
|
||||
class NodeState:
|
||||
def get_value(self, key: str) -> Any
|
||||
def set_value(self, key: str, value: Any)
|
||||
def pop(self, key: str) -> Any
|
||||
def __contains__(self, key: str) -> bool
|
||||
|
||||
# Attribute access
|
||||
cls.state.my_value = 42
|
||||
value = cls.state.my_value
|
||||
|
||||
# Dictionary access
|
||||
cls.state["key"] = "value"
|
||||
value = cls.state["key"]
|
||||
```
|
||||
|
||||
## Practical Input/Output Examples
|
||||
|
||||
### Enhanced Documentation with Tooltips and Display Names
|
||||
|
||||
```python
|
||||
# String input with full documentation
|
||||
io.String.Input(
|
||||
"prompt",
|
||||
display_name="Text Prompt",
|
||||
multiline=True,
|
||||
default="A beautiful landscape",
|
||||
tooltip="Enter the text description for image generation",
|
||||
placeholder="Type your prompt here..."
|
||||
)
|
||||
|
||||
# Integer with constraints and UI hints
|
||||
io.Int.Input(
|
||||
"steps",
|
||||
display_name="Sampling Steps",
|
||||
default=20,
|
||||
min=1,
|
||||
max=150,
|
||||
tooltip="Number of denoising steps. Higher values take longer but may produce better results",
|
||||
display_mode=io.NumberDisplay.slider
|
||||
)
|
||||
|
||||
# Combo with dynamic options
|
||||
io.Combo.Input(
|
||||
"checkpoint",
|
||||
options=folder_paths.get_filename_list("checkpoints"),
|
||||
display_name="Model Checkpoint",
|
||||
tooltip="Select the AI model to use for generation"
|
||||
)
|
||||
|
||||
# Output with documentation
|
||||
io.Image.Output(
|
||||
"generated_image",
|
||||
display_name="Generated Image",
|
||||
tooltip="The final generated image based on your prompt"
|
||||
)
|
||||
|
||||
# Combo with dynamic options and file upload
|
||||
io.Combo.Input(
|
||||
"audio_file",
|
||||
options=sorted(folder_paths.filter_files_content_types(os.listdir(folder_paths.get_input_directory()), ["audio", "video"])),
|
||||
display_name="Audio File",
|
||||
tooltip="Select an audio file or upload a new one",
|
||||
upload=io.UploadType.audio
|
||||
)
|
||||
```
|
||||
|
||||
### Return Pattern with NodeOutput
|
||||
|
||||
```python
|
||||
@classmethod
|
||||
def execute(cls, text: str, count: int) -> io.NodeOutput:
|
||||
# Single output
|
||||
result = process_text(text, count)
|
||||
return io.NodeOutput(result)
|
||||
|
||||
# Multiple outputs
|
||||
image, mask = generate_image_and_mask(text)
|
||||
return io.NodeOutput(image, mask)
|
||||
|
||||
# With UI preview
|
||||
image = generate_image(text)
|
||||
return io.NodeOutput(image, ui=ui.PreviewImage(image))
|
||||
|
||||
# With multiple UI elements
|
||||
images = batch_generate(text, count)
|
||||
previews = [ui.PreviewImage(img) for img in images]
|
||||
return io.NodeOutput(images, ui={"images": previews})
|
||||
```
|
||||
|
||||
## Resource Management
|
||||
|
||||
```python
|
||||
# Load cached resources
|
||||
from comfy_api.latest import resources
|
||||
|
||||
# Load torch file
|
||||
model = cls.resources.get(
|
||||
resources.TorchDictFolderFilename(
|
||||
folder_name="checkpoints", # Folder category
|
||||
file_name="model.safetensors"
|
||||
)
|
||||
)
|
||||
|
||||
# With default value
|
||||
model = cls.resources.get(key, default=None)
|
||||
|
||||
# Custom resource types (future)
|
||||
class MyResourceKey(ResourceKey):
|
||||
Type = MyResourceType
|
||||
def __init__(self, ...):
|
||||
pass
|
||||
```
|
||||
|
||||
## UI Output Classes
|
||||
|
||||
```python
|
||||
from comfy_api.latest import ui
|
||||
|
||||
# Image preview
|
||||
ui.PreviewImage(
|
||||
image: torch.Tensor,
|
||||
animated: bool = False
|
||||
)
|
||||
|
||||
# Mask preview
|
||||
ui.PreviewMask(
|
||||
mask: torch.Tensor,
|
||||
animated: bool = False
|
||||
)
|
||||
|
||||
# Audio preview
|
||||
ui.PreviewAudio(
|
||||
values: list[SavedResult | dict]
|
||||
)
|
||||
|
||||
# Text output
|
||||
ui.PreviewText(
|
||||
value: str
|
||||
)
|
||||
|
||||
# 3D preview
|
||||
ui.PreviewUI3D(
|
||||
values: list[SavedResult | dict]
|
||||
)
|
||||
```
|
||||
|
||||
## Decorators and Helpers
|
||||
|
||||
```python
|
||||
# Create custom ComfyType
|
||||
@io.comfytype(io_type="CUSTOM_TYPE")
|
||||
class CustomType:
|
||||
Type = CustomClass
|
||||
class Input(io.InputV3):
|
||||
...
|
||||
class Output(io.OutputV3):
|
||||
...
|
||||
|
||||
# Custom serializer
|
||||
class MySerializer(Serializer, io_type="MY_TYPE"):
|
||||
@classmethod
|
||||
def serialize(cls, obj: Any) -> str:
|
||||
return json.dumps(obj)
|
||||
|
||||
@classmethod
|
||||
def deserialize(cls, s: str) -> Any:
|
||||
return json.loads(s)
|
||||
```
|
||||
|
||||
## Async Support
|
||||
|
||||
```python
|
||||
# Async execute
|
||||
class AsyncNode(io.ComfyNode):
|
||||
@classmethod
|
||||
async def execute(cls, **kwargs):
|
||||
result = await async_operation()
|
||||
return io.NodeOutput(result)
|
||||
|
||||
# Async validation
|
||||
@classmethod
|
||||
async def VALIDATE_INPUTS(cls, **kwargs):
|
||||
is_valid = await check_validity()
|
||||
return True if is_valid else "Error message"
|
||||
|
||||
# Async lazy check
|
||||
async def check_lazy_status(cls, **kwargs):
|
||||
needed = await determine_needed_inputs()
|
||||
return needed # List of input names
|
||||
```
|
||||
|
||||
## Complete Example
|
||||
|
||||
```python
|
||||
from comfy_api.latest import io, ui, resources, io.ComfyNode, io.ComfyNode
|
||||
import torch
|
||||
|
||||
class AdvancedNodeV3(io.ComfyNode):
|
||||
@classmethod
|
||||
def define_schema(cls):
|
||||
return io.ComfyNode(
|
||||
node_id="AdvancedNode",
|
||||
display_name="Advanced Node",
|
||||
category="examples/advanced",
|
||||
description="Demonstrates v3 features",
|
||||
inputs=[
|
||||
# Basic inputs
|
||||
io.Image.Input("image", tooltip="Input image"),
|
||||
io.Model.Input("model", tooltip="Model to use"),
|
||||
|
||||
# Configured inputs
|
||||
io.Float.Input("strength",
|
||||
default=0.75,
|
||||
min=0.0,
|
||||
max=1.0,
|
||||
step=0.05,
|
||||
display_mode=io.NumberDisplay.slider
|
||||
),
|
||||
|
||||
# Multi-type
|
||||
io.MultiType.Input("flexible",
|
||||
types=[io.Image, io.Mask, io.Latent],
|
||||
optional=True
|
||||
),
|
||||
|
||||
# Dynamic
|
||||
io.AutoGrowDynamic.Input("extra_images",
|
||||
template_input=io.Image.Input("img"),
|
||||
min=0,
|
||||
max=5
|
||||
)
|
||||
],
|
||||
outputs=[
|
||||
io.Image.Output("result", tooltip="Processed image"),
|
||||
io.Latent.Output("latent", is_output_list=True)
|
||||
],
|
||||
hidden=[
|
||||
io.Hidden.unique_id,
|
||||
io.Hidden.prompt
|
||||
],
|
||||
is_output_node=True,
|
||||
is_experimental=True
|
||||
)
|
||||
|
||||
@classmethod
|
||||
async def execute(cls, image, model, strength, flexible=None, **kwargs):
|
||||
# Access state
|
||||
if cls.state.last_model != model:
|
||||
cls.state.cache = {}
|
||||
cls.state.last_model = model
|
||||
|
||||
# Load resources
|
||||
weights = cls.resources.get(
|
||||
resources.TorchDictFolderFilename("loras", "style.safetensors"),
|
||||
default=None
|
||||
)
|
||||
|
||||
# Access hidden
|
||||
node_id = cls.hidden.unique_id
|
||||
|
||||
# Process async
|
||||
result = await process_with_model(image, model, strength)
|
||||
|
||||
# Handle dynamic inputs
|
||||
extra_images = [v for k, v in kwargs.items() if k.startswith("extra_")]
|
||||
|
||||
# Return with UI
|
||||
return io.NodeOutput(
|
||||
result,
|
||||
[latent],
|
||||
ui=ui.PreviewImage(result)
|
||||
)
|
||||
|
||||
@classmethod
|
||||
async def fingerprint_inputs(cls, strength, **kwargs):
|
||||
if strength < 0.1:
|
||||
return "Strength too low for good results"
|
||||
return True
|
||||
```
|
||||
|
||||
## Type Reference
|
||||
|
||||
### Type Mappings
|
||||
|
||||
| v3 Type | Python Type | Shape/Format |
|
||||
|---------|------------|--------------|
|
||||
| `io.Image.Type` | `torch.Tensor` | `[B,H,W,C]` float32 0-1 |
|
||||
| `io.Mask.Type` | `torch.Tensor` | `[H,W]` or `[B,H,W]` float32 |
|
||||
| `io.Latent.Type` | `dict` | `{"samples": tensor, ...}` |
|
||||
| `io.Conditioning.Type` | `list` | `[(tensor, dict), ...]` |
|
||||
| `io.Audio.Type` | `dict` | `{"waveform": tensor, "sample_rate": int}` |
|
||||
| `io.Int.Type` | `int` | Python integer |
|
||||
| `io.Float.Type` | `float` | Python float |
|
||||
| `io.String.Type` | `str` | Python string |
|
||||
| `io.Boolean.Type` | `bool` | Python boolean |
|
||||
|
||||
### Enum Types
|
||||
|
||||
```python
|
||||
# Number display modes
|
||||
io.NumberDisplay.number # Standard input
|
||||
io.NumberDisplay.slider # Slider widget
|
||||
io.NumberDisplay.color # Color picker widget
|
||||
|
||||
# Folder types
|
||||
io.FolderType.input # Input folder
|
||||
io.FolderType.output # Output folder
|
||||
io.FolderType.temp # Temp folder
|
||||
|
||||
# Upload types
|
||||
io.UploadType.image
|
||||
io.UploadType.audio
|
||||
io.UploadType.video
|
||||
io.UploadType.model
|
||||
```
|
||||
Submodule ComfyUI-Easy-Use-Frontend updated: fadf19fe36...e3ee9efe9b
@@ -3,7 +3,8 @@
|
||||
"Hotkeys": "快捷键",
|
||||
"Nodes": "节点相关",
|
||||
"NodesMap": "管理节点组",
|
||||
"StylesSelector": "样式选择器"
|
||||
"StylesSelector": "样式选择器",
|
||||
"MultiAngle": "摄影机多角度提示词"
|
||||
},
|
||||
"nodeCategories": {
|
||||
"Util": "工具",
|
||||
|
||||
@@ -71,5 +71,12 @@
|
||||
"Grid": "网格",
|
||||
"List": "列表"
|
||||
}
|
||||
},
|
||||
"EasyUse_MultiAngle_HollowMode": {
|
||||
"name": "启用多角度镂空展示模式",
|
||||
"tooltip": "在多角度节点中启用镂空展示模式，可以更直观地查看相机角度"
|
||||
},
|
||||
"EasyUse_MultiAngle_AddAnglePrompt": {
|
||||
"name": "启用添加多角度提示词"
|
||||
}
|
||||
}
|
||||
+2
-4
@@ -1,15 +1,13 @@
|
||||
import json
|
||||
import os
|
||||
from urllib.request import urlopen
|
||||
|
||||
import folder_paths
|
||||
|
||||
from .. import easyCache
|
||||
from ..config import FOOOCUS_STYLES_DIR, MAX_SEED_NUM, PROMPT_TEMPLATE, RESOURCES_DIR
|
||||
from ..libs.log import log_node_info
|
||||
from ..libs.utils import AlwaysEqualProxy
|
||||
from ..libs.wildcards import WildcardProcessor, get_wildcard_list, process
|
||||
|
||||
from urllib.request import urlopen
|
||||
from comfy_api.latest import io
|
||||
|
||||
|
||||
@@ -666,7 +664,7 @@ class multiAngle(io.ComfyNode):
|
||||
|
||||
@classmethod
|
||||
def execute(cls, multi_angle=None, **kwargs):
|
||||
if multi_angle is None or not isinstance(multi_angle, list):
|
||||
if multi_angle is None:
|
||||
return io.NodeOutput([""])
|
||||
|
||||
if isinstance(multi_angle, str):
|
||||
|
||||
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+1
-1
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@@ -1,4 +1,4 @@
|
||||
import{G as t,r as e,m as n,t as o,H as i,I as r,J as s,h as l,A as a,K as c,L as u,M as d,D as p,N as h,O as f,i as b,a as m,c as g,y as v,x as y,p as x,E as I,P as k,Q as w,w as O,f as S,z as L,l as $,b as C,s as z,R as E,T,F as _,k as M,d as V,S as P,U as F,V as A}from"./vue-C8DvX6GQ.js";import{c as K,n as D,z as B,A as H,B as j,C as R,D as G,F as U,G as N,H as W,I as q,E as X,b as Y,J as Z,f as J,Z as Q,q as tt,K as et,L as nt,M as ot,N as it,O as rt,P as st,Q as lt,R as at,S as ct,T as ut,w as dt,U as pt,i as ht,V as ft,W as bt,X as mt,Y as gt,_ as vt,$ as yt,a0 as xt,r as It,a1 as kt,a2 as wt,a3 as Ot}from"./primeuix-Zf0UbZPU.js";import{B as St,s as Lt,a as $t,b as Ct,c as zt,U as Et,C as Tt,F as _t,d as Mt,e as Vt,f as Pt,g as Ft,h as At,i as Kt,j as Dt,k as Bt,l as Ht}from"./primevue-B4A7h1ix.js";
|
||||
import{E as t,r as e,m as n,t as o,G as i,H as r,I as s,i as l,z as a,J as c,K as u,L as d,C as p,M as h,N as f,j as b,a as m,c as g,x as v,O as y,p as x,D as I,P as k,Q as w,w as O,f as S,y as L,q as $,b as C,n as z,R as E,T,F as _,k as M,d as V,S as P,U as F,V as A}from"./vue-3zBRwU9X.js";import{c as K,n as D,z as B,A as H,B as j,C as R,D as G,F as U,G as N,H as W,I as q,E as X,b as Y,J as Z,f as J,Z as Q,q as tt,K as et,L as nt,M as ot,N as it,O as rt,P as st,Q as lt,R as at,S as ct,T as ut,w as dt,U as pt,i as ht,V as ft,W as bt,X as mt,Y as gt,_ as vt,$ as yt,a0 as xt,r as It,a1 as kt,a2 as wt,a3 as Ot}from"./primeuix-Zf0UbZPU.js";import{B as St,s as Lt,a as $t,b as Ct,c as zt,U as Et,C as Tt,F as _t,d as Mt,e as Vt,f as Pt,g as Ft,h as At,i as Kt,j as Dt,k as Bt,l as Ht}from"./primevue-CjzBNgZL.js";
|
||||
/*!
|
||||
* pinia v2.2.1
|
||||
* (c) 2024 Eduardo San Martin Morote
|
||||
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Reference in New Issue
Block a user